Abstract: Speaker identification is performed using a single Gaussian mixture model (GMM) for multiple speakers—referred to herein as a Discriminative Gaussian mixture model (DGMM). A likelihood sum of the single GMM is factored into two parts, one of which depends only on the Gaussian mixture model, and the other of which is a discriminative term. The discriminative term allows for the use of a binary classifier, such as a support vector machine (SVM). In one embodiment of the invention, a voice messaging system incorporates a DGMM to identify the speaker who generated a message, if that speaker is a member of a chosen list of target speakers, or to identify the speaker as a “non-target” otherwise.

Abstract: A method and apparatus is described for improving the efficiency of any machine that uses an algorithm that maps to a higher dimensional space in which a given set of vectors is used in a test phase. In particular, reduced set vectors are used. These reduced set vectors are different from the vectors in the set and are determined pursuant to an optimization approach other than the eigenvalue computation used for homogeneous quadratic kernels. An illustrative embodiment is described in the context of a support vector machine (SVM).

Abstract: A kernel-based method and apparatus includes a preprocessor, which operates on an input data in such a way as to provide invariance under some symmetry transformation.

Abstract: A method and system for forming an interpretation of an input expression, where the input expression is expressed in a medium, the interpretation is a sequence of symbols, and each symbol is a symbol in a known symbol set. In general, the system processes an acquired input data set representative of the input expression, to form a set of segments, which are then used to specify a set of consegmentations. Each consegmentation and each possible interpretation for the input expression is represented in a data structure. The data structure is graphically representable by a graph comprising a two-dimensional array of nodes arranged in rows and columns and selectively connected by directed arcs. Each path, extending through the nodes and along the directed arcs, represents one consegmentation and one possible interpretation for the input expression. All of the consegmentations and all of the possible interpretations for the input expression are represented by the set of paths extending through the graph.

Abstract: Noisy images of graphical or pictorial records, such as hand-written postal addresses, are cleaned by a method of analyzing the shapes of pixel clusters. According to such method, a set of structuring kernels is used to extract primitive shapes. The outcome of this extraction process is used to determine whether each pixel, in turn, is to be treated as part of a image element such as an alphanumeric character.